Data-driven (statistical) approaches have been playing an
increasingly prominent role in parsing since the 1990s. In recent
years, there has been a growing interest in dependency-based as
opposed to constituency-based approaches to syntactic parsing, with
application to a wide range of research areas and different languages.
Graph-based and transition-based methods are the two dominant
data-driven approaches to dependency parsing. In a graph-based model,
it defines a space of candidate dependency trees for a given sentence.
Each candidate tree is scored via a local or global scoring function.
The parser (usually uses dynamic programming) outputs the
highest-scored tree. In contrast, in a transition-based model, it
defines a transition system for mapping a sentence to its dependency
tree. It induces a model for predicting the next state transition,
given the transition history. Given the induced model, the output
parse tree is built deterministically upon the construction of the
optimal transition sequence.

Both Graph-based and transition-based approaches have been used to
achieve state-of-the-art dependency parsing results for a wide range
of languages. Some researchers have used the combination of the two
models and it shows the performance of the combined model is
significantly better than the individual models. Another recent trend
is to apply online training to shift-reduce parsing in the
transition-based models. In this tutorial, we first introduce the two
main-stream data-driven dependency parsing models--- graph-based and
transition-based models. After comparing the differences between them,
we show how these two models can be combined in various ways to
achieve better results.

Tutorial Outline

Part A: Introduction to Dependency Parsing

Part B: Graph-based Dependency Parsing Models

Learning Algorithms (Local Learning vs. Global Learning)

Parsing Algorithms (Dynamic Programming)

Features (Static Features vs. Dynamic Features)

Part C: Transition-based Dependency Parsing Models

Learning Algorithms (Local Learning vs. online Learning)

Parsing Algorithms (Shift-reduce Parsing)

Features

Part D: The Combined Models

The stacking Method

The ensemble Method

Single-model Combination

Part E: Other Recent Trends in Dependency Parsing

Integer Linear Programming

Fast Non-Projective Parsing

Instructor Bios

Qin Iris Wang
AT&T Interactiveqiniriswang@gmail.com

Qin Iris Wang is currently a Research Scientist at AT&T Interactive
(San Francisco). Qin obtained her PhD in 2008 from the University of
Alberta under Dekang Lin and Dale Schuurmans. Qin's research interests
include NLP (in particular dependency parsing), machine learning,
information retrieval, text mining and large scale data processing.
Qin's PhD studies was focused on Learning Structured Classifiers for
Statistical Dependency Parsing. Before joined AT&T, she was a research
scientist at Yahoo Labs. Qin was a teaching assistant for two years
during her PhD studies. In 2009, Qin organized a workshop on "
Semi-supervised Learning for Natural Language Processing" at
NAACL-HLT.

Yue Zhang
University of Oxfordyue.zhang@comlab.ox.ac.uk

Yue Zhang just defended his PhD thesis at the University of Oxford.
Yue's research interests include natural language processing (word
segmentation, parsing, machine translation), machine learning, etc.
More specifically, his research area is the syntactic analysis of the
Chinese language, using discriminative machine-learning approaches. He
has worked on word segmentation, joint word segmentation and
POS-tagging, phrase-structure parsing and dependency parsing. Yue
worked on Chinese-English machine-translation during MSc studies in
Oxford, and parallel computing during undergrad studies in Tsinghua
University.